What would the electric system look like if based primarily on renewable energy sources whose output varies with weather and sunlight? Today’s electric system strives to meet three requirements: very high reliability, low cost, and, increasingly since the 1970s, reduced environmental impacts. Due to the design constraints of both climate mitigation and fossil fuel depletion, the possibility of an electric system based primarily on renewable energy is drawing increased attention from analysts.

Several studies (reviewed below) have shown that the solar resource, and the wind resource, are each alone sufficient to power all humankind’s energy needs. Renewable energy will not be limited by resources; on the contrary, the below-cited resource studies show that a shift to renewable power will increase the energy available to humanity.

But how reliable, and how costly, will be an electric system reliant on renewable energy? The common view is that a high fraction of renewable power generation would be costly, and would either often leave us in the dark or would require massive electrical storage.

Good question.

We do not find the answers to the questions posed above in the prior literature. Several studies have shown that global energy demand, roughly 12.5 TW increasing to 17 TW in 2030, can be met with just 2.5% of accessible wind and solar resources, using current technologies [refs below]. Specifically, Delucci and Jacobson pick one mix of eight renewable generation technologies, increased transmission, and storage in grid integrated vehicles (GIV), and show this one mix is sufficient to provide world electricity and fuels. However, these global studies do not assess the ability of variable generation to meet real hourly demand within a single transmission region, nor do they calculate the lowest cost mix of technologies.

This is also what I have found – I’ve read a number of “there’s no barrier to doing this” papers including Delucchi & Jacobson – so I was glad to find this paper. (As an aside, I question some points and assumptions in this paper, but that’s less important and brief comments on those points towards the end).

The key is investigating time series based on real demand for a region and real supply based on the actual wind and sun available.

Before we look at what they did and what they found, here are some comments that are relevant for some of our recent discussions:

In a real grid, we must satisfy varying load, and with high-penetration renewables, charging and discharging storage will at times be limited by power limits not just by stored energy. More typical studies combining wind and solar do not seek any economic analysis and/or do not look at hourly match of generation to load..

Hart and Jacobson determined the least cost mix for California of wind, solar, geothermal and hydro generation. Because their mix includes dispatchable hydro, pumped hydro, geothermal, and solar thermal with storage, their variable generation (wind and photovoltaic solar) never goes above 60% of generation. Because of these existing dispatchable resources, California poses a less challenging problem than most areas elsewhere, most or all practical renewable energy sources are variable generation, and dedicated storage must be purchased for leveling power output. We cannot draw general conclusions from the California case’s results..

The ability to reliably meet load will still be required of systems in the future, despite the variability inherent in most renewable resources. However, a review of existing literature does not find a satisfactory analysis of how to do this with variable generation, nor on a regional grid-operator scale, nor at the least cost. We need to solve for all three.

What does the paper do?

Use the demand load from PJM (East Coast grid operator) for 4 years as a basis for assessing the cost-minimized solution – with the average load being 31.5 GW

Assign a cost (unsubsidized) to each type of renewable resource: onshore wind, offshore wind, solar based on 2008 costs and forecasts for 2030 costs (roughly 50% of 2008 capex costs with similar O&M costs)

And then run through nearly 2 billion or so combinations to first ensure demand is met, then secondly calculate the cost of each combination

From Budischak et al 2013

Figure 1

An most important note for me, something we will review in future articles, rather than here, is the very low cost assigned to storage using vehicle batteries – at $32/kWh, whereas centralized storage is $318/kWh. It’s clear, as we will see, that storage costs skew the analysis strongly.

Here was their lowest cost solution for 30%, 90% and 99.9% renewables. The results are probably not so surprising to people who’ve followed the series so far. Energy Produced GWa is basically the average power over the year (so 8760 GWh, which is a constant 1GW all year = 1 GWa):

From Budischak et al 2013

Figure 2

So we can see that the lowest cost method of matching demand is to produce almost 3 times the required demand. That is, the energy produced across the year averages at 91.3 GW (and appears to have peaks around 200GW). This is because storage costs so much – and because supply is intermittent. Here is the time series – click to expand:

From Budischak et al 2013

Figure 3 – Click to Expand

We see that the energy in storage (middle row) is pulled down in summer, which the paper explains as due to less supply in summer (generally less wind).

Here is a challenging week in detail, the top graph shows the gaps that need to be filled in with storage, the bottom graphs with the gaps filled by storage and also how much supply is “spilled“:

From Budischak et al 2013

Figure 4 – Click to Expand

Here is the mix of generation and storage for each of the 30%, 90%, 99.9% each under the two cost assumptions of 2008 and 2030:

From Budischak et al 2013

Figure 5 – Click to Expand

Looking at the 99.9% cases we see that the projected solar PV cost in 2030 means it has a bigger share compared with wind but that wind is still the dominant power source by a long way. (We will investigate offshore wind costs and reliability in a future article).

Costs

The paper assesses that generating 30% of power from renewables today is already cheaper than conventional generation, and producing 90% in 2030 will be cheaper than conventional generation, with 99.9% at parity.

The key point I would like to draw readers attention to, is that unlike conventional generation, the higher the penetration of renewables the more expensive the solution (because the intermittency is then a bigger problem and so requires a more costly solution).

I’m not clear how they get to the result of renewables already being cheaper than conventional (for a 30% penetration). Their wind power cost from 2008 is roughly double what we found from a variety of sources (see IX – Onshore Wind Costs & XI – Cost of Gas Plants vs Wind Farms) and we found – depending on the gas price and the discount rate – that wind at that price was generally somewhat more expensive than gas. Using current US gas prices this is definitely the case. The authors comment that there are significant subsidies for conventional generation – I have not dug into that as yet.

The cost of storage seems low. If we take instead their cost of centralized storage – $318/kWh – and look at the lowest-cost solution to meet demand we find quite a different result. First, there is a lot less storage – 360 vs 891 GWh. That’s because it’s so pricey.

Second, although the final cost per kWh of energy is not given, we can see that whereas in the GIV storage case we build 16GW solar, 90GW offshore wind, 124GW inland wind = 230GW peak, with centralized storage we build 50, 129, 61 = 240GW peak and probably need the expensive offshore wind as a more reliable (less intermittent) source than onshore wind.

My basic calculation from his data is that the capital cost of the best case central storage solution is 45% more than the GIV storage solution. And more offshore wind will definitely require additional transmission cost (which was not included in the study).

I like their approach. What is clear is that finding the best cost solution depends heavily on the cost of storage, and the mix is radically different for different storage costs. Again, it is the intermittent nature of renewables for the region in question that shapes the result.

Questions on the Analysis

We simplify our grid model by assuming perfect transmission within PJM (sometimes called a “copper plate” assumption), and no transmission to adjacent grids. We also simplify by ignoring reserve requirements, within-hourly fluctuations and ramp rates; these would be easily covered with the amount of fast storage contemplated here. In addition, we assume no preloading of storage from fossil (based on forecasting) and no demand-side management. Adding transmission would raise the costs of the renewable systems calculated here, whereas using adjacent grids, demand management, and forecasting all would lower costs. We judge the latter factors substantially larger, and thus assert (without calculation) that the net effect of adding all these factors together would not raise the costs per kWh above those we calculate below.

Their analysis consumed a lot of computing resources. Adding transmission costs would add another level of complexity. However, I don’t agree with the conclusion that the transmission costs would be offset by adjacent grids, demand management and forecasting.

In brief:

Adjacent grids have the exact same problem – the wind and solar are moving approximately in sync – meaning supply in adjacent regions is quite highly correlated; and hot and cold temperatures are likewise in sync so air-conditioning and heating demand is similar in adjacent regions – therefore another region will be drawing on their storage at the same times as the PJM region. Also, “using adjacent grids” means adding even longer transmission lines of very high capacity. That has a cost.

“Demand management” is possibly a mythical creation to solve the problem of demand being at the “wrong time”. Apart from paying big industrials to turn off power during peak demand, which is already in play for most grid operators, it apparently equates to people not turning on the heating in the cold weather – or to people buying expensive storage. I will be looking for research with some data that puts “demand management” into some reality-based focus.

Forecasting doesn’t exactly help, unless you have demand management. Better wind forecasting currently helps grid operators because it allows them to buy reserve (conventional generation) at the right time, making a more efficient use of conventional generation. I can’t see how it helps a mostly renewable scenario to be more cost-effective. Perhaps someone can explain to me what I am missing.

And I will dig into storage costs in a future article.

Conclusion

The paper is very good overall – their approach is the important aspect. There are a great many papers which all confidently state that there is no technical barrier to 100% renewables. This is true. But maybe two or three papers is enough.

If you add “enough” wind farms and “enough” solar and “enough” storage – along with “enough” transmission – you can make the grid work. But what is the cost and how exactly are you going to solve the problems? After the first few papers to consider this question, any subsequent ones that don’t actually cover the critical problem of electricity grids with intermittent renewables are basically a waste of time.

What is the critical problem? Given that storage is extremely expensive, and given the intermittent nature of renewables with the worst week of low sun and low wind in a given region – how do you actually make it work? Because yes, there is a barrier to making a 100% renewable network operate reliably. It’s not technical, as such, not if you have infinite money..

It should be crystal clear that if you need 500GW of average supply to run the US you can’t just build 500GW of “nameplate” renewable capacity. And you can’t just build 500GW / capacity factor of renewable capacity (e.g. if we required 500GW just from wind we would build something like 1.2-1.5TW due to the 30-40% capacity factor of wind) and just add “affordable storage”.

So, there is no technical barrier to powering the entire US from a renewable grid with lots of storage. Probably $50TR will be enough for the storage. Or forget the storage and just build 10x the nameplate of wind farms and have a transmission grid of 500GW around the entire country. Probably the 5TW of wind farms will only cost $5TR and the redundant transmission grid will only cost $20TR – so that’s only $25TR.

Hopefully, the point is clear. It’s a different story from dispatchable conventional generation. Adding up the possible total energy from wind and solar is step 1 and that’s been done multiple times. The critical item, missing from many papers, is to actually analyze the demand and supply options with respect to a time series and find out what is missing. And find some sensible mix of generation and storage (and transmission, although that was not analyzed in this paper) that matches supply and demand.

So this paper has a lot of merit.

It shows with their storage costs (which seem very low), that the lowest cost solution to building a 99.9% renewable network in one (reasonable sized) region is to build nearly 3 times the actual supply needed (this is not a “capacity factor” issue – see note 2).

In future articles we will look at storage costs, as I have questions about their costing. But the main points from this paper are more than enough for one article.

Notes

Note 2: The 2-3 overbuilding is not the nameplate vs capacity factor question. Let me explain. Imagine we are only talking about wind. If we build 3GW of wind farms we might get 1GW of average output across a year. This is a 33% capacity factor. The % depends on the wind turbines and where they are located.

Now if we need to get 1GW average across the year and meet demand 99.9% of the time, the lowest cost solution won’t be to build 3GW of nameplate (=1GW of average output) and add lots of storage, instead it will be to build 9GW of nameplate and some storage.

In earlier articles we looked at wind power, what it costs, what it does to the grid, and what to do when the wind is not blowing.

Now a frequent comment – which conceals more than it reveals – is: “the wind always blows somewhere”. This is true – if you have lots of wind farms that are geographically dispersed you do average out your peaks and troughs, and you do also reduce the % change hour by hour.

However, if you have 20% of your average power coming from wind, then on one given day it might be 60% of your requirements, yet the next day it might be 0.3%. This means that sometimes you are “winding back” your conventional generation, and sometimes you are “cranking up” your conventional generation – and much more in absolute terms than in a network of 98%+ conventional generation. The larger the penetration of wind energy the more problems this causes.

The question has come up a few times without being answered – what is the impact on efficiency of conventional power generation?

It’s clear that the impact depends on the penetration of wind. Very recent analysis is hard to find.

It is important to understand that the key issue is not whether a system with a significant amount of wind capacity can be operated reliably, but rather to what extent the system operating costs are increased by the variability of the wind..

..Over the past two years, several investigations of these questions have been conducted by or on behalf of U.S. electric utilities. These studies addressed utility systems with different generating resource mixes and employed different analytical approaches. In aggregate, this work provides illuminating insights into the issue of wind’s impacts on overall electric system operating costs.

I extracted two useful examples from the NREL study:

PacifiCorp:

PacifiCorp, a large utility in the northwestern United States, operates a system with a peak load of 8,300 MW that is expected to grow to 10,000 MW over the next decade. PacifiCorp recently completed an Integrated Resource Plan (IRP) that identified 1,400 MW (14%) of wind capacity over the next 10 years as part of the least-cost resource portfolio.

A number of studies were performed to estimate the cost of wind integration on its system. The costs were categorized as incremental reserve or imbalance costs. Incremental reserves included the cost associated with installation of additional operating reserves to maintain system reliability at higher levels of wind penetration, recognizing the incremental variability in system load imposed by the variability of wind plant output.

Imbalance costs captured the incremental operating costs associated with different amounts of wind energy compared to the case without any wind energy.

At wind penetration levels of 2,000 MW (20%) on the PacifiCorp system, the average integration costs were $5.50/MWh, consisting of an incremental reserve component of $2.50 and an imbalance cost of $3.00. The cost of additional regulating reserve was not considered. These costs are considered by PacifiCorp to be a reasonable approximation to the costs of integrating the wind capacity.

Great River Energy:

Great River Energy (GRE) is a Generation and Transmission electric cooperative serving parts of Minnesota and northeast Wisconsin. It is primarily a thermal system in the Mid- Continent Area Power Pool (MAPP) region with a summer peak load in excess of 2300 MW, growing at 3%-4% per year.. As part of its planning process to meet this objective, GRE performed a study with Electrotek that examined adding 500 MW of wind in 100 MW increments between now and 2015. GRE operates with a fixed fleet of generation and uses a static scheduling process, so it did not decompose the problem into the three time periods commonly used in the analysis of ancillary-service costs in larger utilities. It also looked at providing the ancillary services required from its own resources, including a 600-MW combined-cycle unit, which was subsequently cancelled. GRE found ancillary- service costs of $3.19/MWh at 4.3% penetration and $4.53/MWh at 16.6% penetration. It is likely that the costs would have been higher without the combined-cycle unit and self-providing the ancillary services without economical intermediate resources.

It appears that these studies are based on nameplate values (I didn’t find an explicit statement but the wording implies it and later references to the data agree). That’s a pretty big difference, because in GRE’s case it means that the “16.6%” would actually be something like “5-6% of average electricity production from wind”. The 16.6% would be when the wind farms were operating at their maximum.

It seems like there should be many more studies, especially given the increase in wind penetration of electricity networks in Germany, UK and Ireland. However, many references work their way back to the same papers. For example, Overview of wind power intermittency impacts on power systems, MH Albadi & EF El-Saadany, Electric Power Systems Research (2010) says:

Smith et al. reported that the existing case studies have explored wind capacity penetrations of up to 20–30% of system peak and have found that the primary considerations are economic, not physical [9].

The reference [9] is Utility Wind Integration and Operating Impact State of the Art, J Smith et al, IEEE Transactions on Power Systems (2007), which states:

On the cost side, at wind penetrations of up to 20% of system peak demand, it has been found that system operating cost increases arising from wind variability and uncertainty amounted to about 10% or less of the wholesale value of the wind energy [2]. This finding will need to be reexamined as the results of higher-wind-penetration studies—in the range of 25% to 30% of peak balancing-area load—become available. However, achieving such penetrations is likely to require one or two decades.

The reference [2] here is Wind plant integration, E DeMeo et al, IEEE Power Energy Mag (2005) which has the same data as the NREL study, not surprising as two of the authors are the same.

Albadi & EF El-Saadany 2010 compile some data, note the reference again is to peak penetration:

From Albadi & El-Saadany 2010

Figure 1

We can see a big range. For example:

the UK costs at the top of the graph, with peak penetration of 20-40% (=average penetration of 6-12%) having costs of around $5/MWh, or 0.5c/kWh

the Finland costs at the bottom right with 32-65% (=average of 10-20%, but I’m unsure of their capacity factor) having costs of around $1/MWh, or 0.1c/kWh

The study that produced these particular values (and some others) is H. Holttinen et al 2009. This is from 2009 and what appears to be the same data is in a paper in Wind Energy (2011). However, the studies that produced their data: Finland and Nordic – PhD by Holtinnen 2004; Sweden – paper by U Axelsson et al from 2005; Ireland – 2004 study; UK – paper by Strbac et al from 2007; Germany – Dena Grid study from 2005; Minnesota – paper for Minnesota Public Utilities Commission from 2006; and California – paper by Porter et al from 2007.

Holtinnen et al 2011 summary:

From the cost estimates presented in the investigated studies it follows that at wind penetrations of up to 20 % of gross demand (energy), system operating cost increases arising from wind variability and uncertainty amounted to about 1–4 €/MWh of wind power produced (Fig. 5). This is 10 % or less of the wholesale value of the wind energy. The actual impact of adding wind generation in different balancing areas can vary depending on local factors. Important factors identified to reduce integration costs are aggregating wind plant output over large geographical regions, larger balancing areas, and utilizing shorter gate closure times with accurate forecast systems and sub-hourly schedule changes.

An important point, often missed by pundits looking at Denmark:

The interconnection capacity to neighbouring systems is often significant. For the balancing costs, it is then essential to note in the study setup whether the interconnection capacity can be used for balancing purposes or not. A general conclusion is that if interconnection capacity is allowed to be used also for balancing purposes, then the balancing costs are lower compared to the case where they are not allowed to be used.

The two points for Greennet Germany at the same wind penetration level reflect that balancing costs increase when neighbouring countries get more wind (the same applies for Greennet Denmark). For a small part of an interconnected system, a wind integration study stating a high penetration level can also be misleading if the wind penetration in neighbouring areas is low and interconnection capacity plays a major part in integration.

They have many interesting points in their paper:

In Denmark the TSO has estimated the impacts of increasing the wind penetration level from 20 % to 50 % (of gross demand) and concluded that further large scale integration of wind power calls for exploiting both, domestic flexibility and international power markets with measures on the market side, production side, transmission side and demand side ([19] and [20]).

This kind of implies there are big issues, but the documentation is locked away in conference proceedings. Surely some published papers have come out of this important question so I will continue to dig..

A digression, for people concerned that wind power research and costing ignores transmission costs, another (counter-) example:

Transmission cost is the extra cost in the transmission system when wind power is integrated. Either all extra costs are allocated to wind power, or only part of the extra costs are allocated to wind power – grid reinforcements and new transmission lines often benefit also other consumers or producers and can be used for many purposes, such as increase of reliability and/or increased trading. The cost of grid reinforcements due to wind power is therefore very dependent on where the wind power plants are located relative to load and on the grid infrastructure, and one must expect numbers to vary from country to country.

Grid reinforcement costs are by nature dependent on the existing grid. The costs vary with time and are dependent on when the generator is connected. After building some lines, often several generators can be connected before new reinforcement needs occur. After a certain time, new lines, substations or something else is needed.

The grid reinforcement costs are not continuous; there can be single very high cost reinforcements. Using higher voltages generally results in lower costs per MW transported but this also means that there are even higher increments of capacity and grid costs. The same wind power plant, connected at different times, may therefore lead to different grid reinforcement costs. For transmission planning, the most cost effective solution in cases that require considerable grid reinforcements would be to build the transmission network for the final planned amount of wind power in the network – instead of having to upgrade transmission lines in several phases.

Conclusion

It seems like everyone studying wind power believes the additional costs incurred as a result of having to ramp up and down conventional power systems are relatively low – typically less than 0.5c/KWh for 20% penetration. Likewise, everyone agrees that there is a real cost to be paid. The cost for 50% penetration is unclear, even if it is feasible.

There doesn’t seem to be any real world data for high wind penetrations, which is not surprising as Germany, a wind power leader, has only about 10% of (annual average) power coming from wind, and Denmark is effectively part of a much larger grid (by virtue of interconnection).

Whether or not the current estimates factor in the lifetime impact on power stations (due to lots more heating and cooling causing more stressing of various parts of the system) is something that might only be found by the real world experiment of doing it for a couple of decades.

[Note that many statements and press releases on the subject of wind do not clarify whether they are talking about “peak”, i.e., nameplate, or “average”, i.e. the nameplate x capacity factor – it is essential to clarify this before putting any weight on the claim].